





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
	lowerCAmelCase_        =							argparse.ArgumentParser(
	    description=(
	        '''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
	        ''' Distillation'''
	    )
	)
	parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
	parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
	parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
	parser.add_argument('''--vocab_transform''', action='''store_true''')
	lowerCAmelCase_        =							parser.parse_args()

	if args.model_type == "roberta":
		lowerCAmelCase_        =							RobertaForMaskedLM.from_pretrained(args.model_name)
		lowerCAmelCase_        =							'''roberta'''
	elif args.model_type == "gpt2":
		lowerCAmelCase_        =							GPTaLMHeadModel.from_pretrained(args.model_name)
		lowerCAmelCase_        =							'''transformer'''

	lowerCAmelCase_        =							model.state_dict()
	lowerCAmelCase_        =							{}

	# Embeddings #
	if args.model_type == "gpt2":
		for param_name in ["wte.weight", "wpe.weight"]:
			lowerCAmelCase_        =							state_dict[F'''{prefix}.{param_name}''']
	else:
		for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
			lowerCAmelCase_        =							F'''{prefix}.embeddings.{w}.weight'''
			lowerCAmelCase_        =							state_dict[param_name]
		for w in ["weight", "bias"]:
			lowerCAmelCase_        =							F'''{prefix}.embeddings.LayerNorm.{w}'''
			lowerCAmelCase_        =							state_dict[param_name]

    # Transformer Blocks #
	lowerCAmelCase_        =							0
	for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
		if args.model_type == "gpt2":
			for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
				for w in ["weight", "bias"]:
					lowerCAmelCase_        =							state_dict[
					    F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
					]
			lowerCAmelCase_        =							state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
		else:
			for layer in [
			    "attention.self.query",
			    "attention.self.key",
			    "attention.self.value",
			    "attention.output.dense",
			    "attention.output.LayerNorm",
			    "intermediate.dense",
			    "output.dense",
			    "output.LayerNorm",
			]:
				for w in ["weight", "bias"]:
					lowerCAmelCase_        =							state_dict[
					    F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
					]
		std_idx += 1

	# Language Modeling Head ###s
	if args.model_type == "roberta":
		for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
			lowerCAmelCase_        =							state_dict[F'''{layer}''']
		if args.vocab_transform:
			for w in ["weight", "bias"]:
				lowerCAmelCase_        =							state_dict[F'''lm_head.dense.{w}''']
				lowerCAmelCase_        =							state_dict[F'''lm_head.layer_norm.{w}''']
	elif args.model_type == "gpt2":
		for w in ["weight", "bias"]:
			lowerCAmelCase_        =							state_dict[F'''{prefix}.ln_f.{w}''']
		lowerCAmelCase_        =							state_dict['''lm_head.weight''']

	print(F'''N layers selected for distillation: {std_idx}''')
	print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

	print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
	torch.save(compressed_sd, args.dump_checkpoint)

def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				word.split()

				def justify(lowercase				,     lowercase				,     lowercase				) -> str:
								__lowercase					     =				max_width - width
								__lowercase					     =				len(lowercase				)
								if len(lowercase				) == 1:
												# if there is only word in line
												# just insert overall_spaces_count for the remainder of line
												return line[0] + " " * overall_spaces_count
								else:
												__lowercase					     =				words_count - 1
												# num_spaces_between_words_list[i] : tells you to insert
												# num_spaces_between_words_list[i] spaces
												# after word on line[i]
												__lowercase					     =				spaces_to_insert_between_words * [
												    overall_spaces_count // spaces_to_insert_between_words
												]
												__lowercase					     =				(
												    overall_spaces_count % spaces_to_insert_between_words
												)
												# distribute spaces via round robin to the left words
												for i in range(lowercase				):
																num_spaces_between_words_list[i] += 1
												__lowercase					     =				[]
												for i in range(lowercase				):
																# add the word
																aligned_words_list.append(line[i]				)
																# add the spaces to insert
																aligned_words_list.append(num_spaces_between_words_list[i] * ''' '''				)
												# just add the last word to the sentence
												aligned_words_list.append(line[-1]				)
												# join the aligned words list to form a justified line
												return "".join(lowercase				)

				__lowercase					     =				[]
				__lowercase					     =				[]
				__lowercase					     =				0
				for word in words:
								if width + len(lowercase				) + len(lowercase				) <= max_width:
												# keep adding words until we can fill out max_width
												# width = sum of length of all words (without overall_spaces_count)
												# len(word) = length of current word
												# len(line) = number of overall_spaces_count to insert between words
												line.append(lowercase				)
												width += len(lowercase				)
								else:
												# justify the line and add it to result
												answer.append(justify(lowercase				,     lowercase				,     lowercase				)				)
												# reset new line and new width
												__lowercase     ,	__lowercase					     =				[word], len(lowercase				)
				__lowercase					     =				max_width - width - len(lowercase				)
				answer.append(''' '''.join(lowercase				) + (remaining_spaces + 1) * ''' '''				)
				return answer


if __name__ == "__main__":
		from doctest import testmod

		testmod()